Rapid and accurate single-cell detection of foodborne pathogens using Raman spectroscopy enhanced by Raman Mel spectrograms and generative adversarial networks

Foodborne pathogens pose a significant threat to food safety. Therefore, the rapid and accurate detection of these pathogens is of critical importance. Raman spectroscopy, as a rapid, non-invasive, and label-free analytical technique, demonstrates significant potential for their detection. However, in scenarios requiring single-cell level rapid detection, Raman spectroscopy suffers from limited sensitivity. This limitation arises from the short integration times, the inherently weak Raman signal, and the extremely small volume of individual bacterial cells. To address this challenge, this study proposes a spectral restoration approach that combines Mel spectrogram transformation with a generative adversarial network (GAN_Mel). In this approach, the spectrogram transformation process is integrated into the discriminator architecture of the GAN to improve reconstruction efficiency and denoising capability. Experimental results demonstrate that, under a short integration time of 3 seconds, the proposed method can effectively enhance the spectral signal-to-noise ratio (SNR) to a level comparable to that obtained with a 30-second integration time, achieving a classification accuracy of 92.9% for foodborne bacterial species. This study provides a solid technical foundation for the rapid and accurate single-cell detection of foodborne pathogens using Raman spectroscopy. • A novel GAN_Mel Raman spectral denoising model was developed. • The model demonstrated superior performance and strong robustness. • GAN_Mel achieved 30 s spectral quality from 3 s acquisitions. • Classification accuracy increased from 54.3% to 92.9% after GAN_Mel processing. • Our method enables rapid and accurate single-cell detection of foodborne pathogens.